Bibliographic record
Abstract
Images issued from a SAR (Synthetic Aperture Radar) sensor are effected by a specific noise called speckle; therefore, many studies have been dedicated to modulate this noise with the aim to be able to reduce its effects. But, studies in the area of polarimetric SAR (PolSAR) images despeckling are still poor and don't take advantage correctly of polarimetric information. In this way, this paper describes an original and efficient method of despeckling PolSAR images in order to improve the visualization and the extraction of planimetric features. The proposed filter, takes into account all polarization modes for each polarization mode despeckling. So, for a pixel in a single polarization mode, the modification of its radiometric value will be supervised by it adjacent pixels in the same polarization mode and also by their equivalent pixels in other polarization modes. Furthermore, to avoid error propagation, the filter will be very cautious in modification of radiometric values in such way that it runs in many iterations modifying the less ambiguous pixels firstly and leaves the rest of the pixels for the next iterations for a possible modification. To combine the information resulting from each polarization mode and make a decision, the proposed filter calls some rules of the Evidence Theory. The experimentation was done on Radarsat-2 images of the Arctic and Quebec regions of Canada, and the results show clearly the benefit and the high performance of this despeckling approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".